Lag rank

Calculate the mean rank of absolute input position lags between adjacent recalls, relative to possible absolute lags.

Usage

lag_rank(
  data,
  lag_key = "input",
  item_query = NULL,
  test_key = NULL,
  test = NULL
)

Arguments

data

Merged study and recall data.

lag_key

Name of column to use when calculating lag between recalled items.

item_query

Query string to select items to include in the pool of possible recalls to be examined.

test_key

Name of column with labels to use when testing transitions for inclusion.

test

Function that takes in previous and current item values and returns TRUE for transitions that should be included.

Value

Results with subject and rank columns. The rank indicates how strongly recalls were clustered by input position lag relative to the possible items that could have been recalled on each transition. A rank

of 1 indicates that the lowest lag item was always recalled. A rank of 0.5 indicates chance clustering. A rank of 0 indicates that the highest lag item was always recalled.

Examples

raw <- sample_data("Morton2013")
data <- merge_free_recall(raw, study_keys = list("category"))
head(lag_rank(data))
#>   subject      rank
#> 1       1 0.6109533
#> 2       2 0.6356764
#> 3       3 0.6126071
#> 4       4 0.6670897
#> 5       5 0.6439234
#> 6       6 0.6484440